Exploring Capabilities for the Smart Service Transformation in Manufacturing: Insights from Theory and Practice - Koldewey, Christian; Fichtler, Timm; Scholtysik, Michel; Biehler, Jan; Schreiner, Nick; Sommer, Franziska; Schacht, Maximilian; Kaufmann, Jonas; Rabe, Martin; Sedlmeier, Joachim; Dumitrescu, Roman
Digital Servitization is one of the significant trends affecting the manufacturing industry. Companies try to tackle challenges regarding their differentiation and profitability using digital services. One specific type of digital services are smart services, which are digital services built on data from smart products. Introducing these kinds of offerings into the portfolio of manufacturing companies is not trivial. Moreover, they require conscious action to align all relevant capabilities to realize the respective business goals. However, what capabilities are generally relevant for smart services remains opaque. We conducted a systematic literature review to identify them and extended the results through an interview study. Our analysis results in 78 capabilities clustered among 12 principles and six dimensions. These results provide significant support for the smart service transformation of manufacturing companies and for structuring the research field of smart services.
Keywords: Digital Servitization; Transformation; Capabilities; Maturity; Smart Services

Digital Security -- A Question of Perspective. A Large-Scale Telephone Survey with Four At-Risk User Groups - Herbert, Franziska; Becker, Steffen; Buckmann, Annalina; Kowalewski, Marvin; Hielscher, Jonas; Acar, Yasemin; Dürmuth, Markus; Sasse, M. Angela; Zou, Yixin (IEEE Symposium on Security and Privacy. IEEE, New York, NY, USA)

Domain Knowledge Injection Guidance for Predictive Maintenance - Afroze, Lameya; Merkelbach, Silke; von Enzberg, Sebastian; Dumitrescu, Roman (ML4CPS 2023)

Business strategy taxonomy and solution patterns for the circular economy - Scholtysik, Michel; Rohde, Malte; Koldewey, Christian; Dumitrescu, Roman

X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics - Dou, Feng; Wang, Lin; Chen, Shutong; Liu, Fangming (Proceedings of the IEEE International Conference on Computer Communications (INFOCOM))

Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES - Blöcher, Marcel; Nedderhut, Nils; Chuprikov, Pavel; Khalili, Ramin; Eugster, Patrick; Wang, Lin (Proceedings of the IEEE International Conference on Computer Communications (INFOCOM))

𝜆Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing - Hu, Haichuan; Liu, Fangming; Pei, Qiangyu; Yuan, Yongjie; Xu, Zichen; Wang, Lin (Proceedings of the ACM Web Conference (WWW))

Extended Dynamic Mode Decomposition: Sharp bounds on the sample efficiency - Philipp, Friedrich M.; Schaller, Manuel; Boshoff, Septimus; Peitz, Sebastian; Nüske, Feliks; Worthmann, Karl (arXiv:2402.02494)
We rigorously derive novel and sharp finite-data error bounds for highly sample-efficient Extended Dynamic Mode Decomposition (EDMD) for both i.i.d. and ergodic sampling. In particular, we show all results in a very general setting removing most of the typically imposed assumptions such that, among others, discrete- and continuous-time stochastic processes as well as nonlinear partial differential equations are contained in the considered system class. Besides showing an exponential rate for i.i.d. sampling, we prove, to the best of our knowledge, the first superlinear convergence rates for ergodic sampling of deterministic systems. We verify sharpness of the derived error bounds by conducting numerical simulations for highly-complex applications from molecular dynamics and chaotic flame propagation.

Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems - Sonntag, Konstantin; Peitz, Sebastian (Journal of Optimization Theory and Applications)
We derive efficient algorithms to compute weakly Pareto optimal solutions for smooth, convex and unconstrained multiobjective optimization problems in general Hilbert spaces. To this end, we define a novel inertial gradient-like dynamical system in the multiobjective setting, which trajectories converge weakly to Pareto optimal solutions. Discretization of this system yields an inertial multiobjective algorithm which generates sequences that converge weakly to Pareto optimal solutions. We employ Nesterov acceleration to define an algorithm with an improved convergence rate compared to the plain multiobjective steepest descent method (Algorithm 1). A further improvement in terms of efficiency is achieved by avoiding the solution of a quadratic subproblem to compute a common step direction for all objective functions, which is usually required in first-order methods. Using a different discretization of our inertial gradient-like dynamical system, we obtain an accelerated multiobjective gradient method that does not require the solution of a subproblem in each step (Algorithm 2). While this algorithm does not converge in general, it yields good results on test problems while being faster than standard steepest descent.

A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces - Sonntag, Konstantin; Gebken, Bennet; Müller, Georg; Peitz, Sebastian; Volkwein, Stefan (arXiv:2402.06376)
The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from [1] is extended from finite-dimensional problems to general Hilbert spaces. The method iteratively computes Pareto critical points, where in each iteration, an approximation of the subdifferential is computed in an efficient manner and then used to compute a common descent direction for all objective functions. To prove convergence, we present some new optimality results for nonsmooth multiobjective optimization problems in Hilbert spaces. Using these, we can show that every accumulation point of the sequence generated by our algorithm is Pareto critical under common assumptions. Computational efficiency for finding Pareto critical points is numerically demonstrated for multiobjective optimal control of an obstacle problem.

Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning - Peitz, Sebastian; Stenner, Jan; Chidananda, Vikas; Wallscheid, Oliver; Brunton, Steven L.; Taira, Kunihiko (Physica D: Nonlinear Phenomena)
We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs). Exploiting translational equivariances, the high-dimensional distributed control problem can be transformed into a multi-agent control problem with many identical, uncoupled agents. Furthermore, using the fact that information is transported with finite velocity in many cases, the dimension of the agents’ environment can be drastically reduced using a convolution operation over the state space of the PDE, by which we effectively tackle the curse of dimensionality otherwise present in deep reinforcement learning. In this setting, the complexity can be flexibly adjusted via the kernel width or by using a stride greater than one (meaning that we do not place an actuator at each sensor location). Moreover, scaling from smaller to larger domains – or the transfer between different domains – becomes a straightforward task requiring little effort. We demonstrate the performance of the proposed framework using several PDE examples with increasing complexity, where stabilization is achieved by training a low-dimensional deep deterministic policy gradient agent using minimal computing resources.

Implementierung eines Algorithmus zur motivbasierten Schnitt-Sparsifizierung - Dorociak, Svitlana

Toward an Android Static Analysis Approach for Data Protection - Khedkar, Mugdha; Bodden, Eric (Proceedings of the 9th International Conference on Mobile Software Engineering and Systems)
Android applications collecting data from users must protect it according to the current legal frameworks. Such data protection has become even more important since the European Union rolled out the General Data Protection Regulation (GDPR). Since app developers are not legal experts, they find it difficult to write privacy-aware source code. Moreover, they have limited tool support to reason about data protection throughout their app development process. This paper motivates the need for a static analysis approach to diagnose and explain data protection in Android apps. The analysis will recognize personal data sources in the source code, and aims to further examine the data flow originating from these sources. App developers can then address key questions about data manipulation, derived data, and the presence of technical measures. Despite challenges, we explore to what extent one can realize this analysis through static taint analysis, a common method for identifying security vulnerabilities. This is a first step towards designing a tool-based approach that aids app developers and assessors in ensuring data protection in Android apps, based on automated static program analysis.
Keywords: static program analysis; data protection and privacy; GDPR compliance

Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories - Otto, Samuel E.; Peitz, Sebastian; Rowley, Clarence W. (SIAM Journal on Applied Dynamical Systems)
Data-driven models for nonlinear dynamical systems based on approximating the underlying Koopman operator or generator have proven to be successful tools for forecasting, feature learning, state estimation, and control. It has become well known that the Koopman generators for control-affine systems also have affine dependence on the input, leading to convenient finite-dimensional bilinear approximations of the dynamics. Yet there are still two main obstacles that limit the scope of current approaches for approximating the Koopman generators of systems with actuation. First, the performance of existing methods depends heavily on the choice of basis functions over which the Koopman generator is to be approximated; and there is currently no universal way to choose them for systems that are not measure preserving. Secondly, if we do not observe the full state, we may not gain access to a sufficiently rich collection of such functions to describe the dynamics. This is because the commonly used method of forming time-delayed observables fails when there is actuation. To remedy these issues, we write the dynamics of observables governed by the Koopman generator as a bilinear hidden Markov model, and determine the model parameters using the expectation-maximization (EM) algorithm. The E-step involves a standard Kalman filter and smoother, while the M-step resembles control-affine dynamic mode decomposition for the generator. We demonstrate the performance of this method on three examples, including recovery of a finite-dimensional Koopman-invariant subspace for an actuated system with a slow manifold; estimation of Koopman eigenfunctions for the unforced Duffing equation; and model-predictive control of a fluidic pinball system based only on noisy observations of lift and drag.

Supporting Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability - Wickert, Anna-Katharina; Schlichtig, Michael; Vogel, Marvin; Winter, Lukas; Mezini, Mira; Bodden, Eric
Context Static analyses are well-established to aid in understanding bugs or vulnerabilities during the development process or in large-scale studies. A low false-positive rate is essential for the adaption in practice and for precise results of empirical studies. Unfortunately, static analyses tend to report where a vulnerability manifests rather than the fix location. This can cause presumed false positives or imprecise results. Method To address this problem, we designed an adaption of an existing static analysis algorithm that can distinguish between a manifestation and fix location, and reports error chains. An error chain represents at least two interconnected errors that occur successively, thus building the connection between the fix and manifestation location. We used our tool CogniCryptSUBS for a case study on 471 GitHub repositories, a performance benchmark to compare different analysis configurations, and conducted an expert interview. Result We found that 50 % of the projects with a report had at least one error chain. Our runtime benchmark demonstrated that our improvement caused only a minimal runtime overhead of less than 4 %. The results of our expert interview indicate that with our adapted version participants require fewer executions of the analysis. Conclusion Our results indicate that error chains occur frequently in real-world projects, and ignoring them can lead to imprecise evaluation results. The runtime benchmark indicates that our tool is a feasible and efficient solution for detecting error chains in real-world projects. Further, our results gave a hint that the usability of static analyses may benefit from supporting error chains.
Keywords: Static analysis; error chains; false positive re- duction; empirical studies

Post-configuration Activation of Hardware Trojans in FPGAs - Ahmed, Qazi Arbab; Wiersema, Tobias; Platzner, Marco (Journal of Hardware and Systems Security)
Keywords: General Engineering; Energy Engineering and Power Technology

Using Worked Examples for Engaging in Epistemic Programming Projects - Hüsing, Sven; Schulte, Carsten; Sparmann, Sören; Bolte, Mario (Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1)

Differentially Private Natural Language Models: Recent Advances and Future Directions - Hu, Lijie; Habernal, Ivan; Shen, Lei; Wang, Di (Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024)

DP-NMT: Scalable Differentially Private Machine Translation - Igamberdiev, Timour; Vu, Doan Nam Long; Kuennecke, Felix; Yu, Zhuo; Holmer, Jannik; Habernal, Ivan (Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations)
Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community.

Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling - Razavi, Kamran; Ghafouri, Saeid; Mühlhäuser, Max; Jamshidi, Pooyan; Wang, Lin (Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024)

Causal Question Answering with Reinforcement Learning - Blübaum, Lukas; Heindorf, Stefan (The World Wide Web Conference (WWW))

A Dispersion Algorithm for Robot Swarms Inside Polygonal Boundary Shapes - De Groote, Carsten

Optimal Drone Strategies For Packet Delivery - Doddegowda, Rajesh

Evaluating the Implications - Thakur, Heena

On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems - Preuß, Oliver Ludger; Rook, Jeroen; Trautmann, Heike (Applications of Evolutionary Computation)

Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP - Vinzent Seiler, Moritz; Rook, Jeroen; Heins, Jonathan; Preuß, Oliver Ludger; Bossek, Jakob; Trautmann, Heike (2023 IEEE Symposium Series on Computational Intelligence (SSCI))

Error bounds for kernel-based approximations of the Koopman operator - Philipp, Friedrich; Schaller, Manuel; Worthmann, Karl; Peitz, Sebastian; Nüske, Feliks (Applied and Computational Harmonic Analysis)
We consider the data-driven approximation of the Koopman operator for stochastic differential equations on reproducing kernel Hilbert spaces (RKHS). Our focus is on the estimation error if the data are collected from long-term ergodic simulations. We derive both an exact expression for the variance of the kernel cross-covariance operator, measured in the Hilbert-Schmidt norm, and probabilistic bounds for the finite-data estimation error. Moreover, we derive a bound on the prediction error of observables in the RKHS using a finite Mercer series expansion. Further, assuming Koopman-invariance of the RKHS, we provide bounds on the full approximation error. Numerical experiments using the Ornstein-Uhlenbeck process illustrate our results.

Characterizing Microheterogeneity in Liquid Mixtures via Local Density Fluctuations - Lass, Michael; Kenter, Tobias; Plessl, Christian; Brehm, Martin (Entropy)
We present a novel approach to characterize and quantify microheterogeneity and microphase separation in computer simulations of complex liquid mixtures. Our post-processing method is based on local density fluctuations of the different constituents in sampling spheres of varying size. It can be easily applied to both molecular dynamics (MD) and Monte Carlo (MC) simulations, including periodic boundary conditions. Multidimensional correlation of the density distributions yields a clear picture of the domain formation due to the subtle balance of different interactions. We apply our approach to the example of force field molecular dynamics simulations of imidazolium-based ionic liquids with different side chain lengths at different temperatures, namely 1-ethyl-3-methylimidazolium chloride, 1-hexyl-3-methylimidazolium chloride, and 1-decyl-3-methylimidazolium chloride, which are known to form distinct liquid domains. We put the results into the context of existing microheterogeneity analyses and demonstrate the advantages and sensitivity of our novel method. Furthermore, we show how to estimate the configuration entropy from our analysis, and we investigate voids in the system. The analysis has been implemented into our program package TRAVIS and is thus available as free software.

Optimisation and Evaluation of Breadth First Search with oneAPI/SYCL on Intel FPGAs: from Describing Algorithms to Describing Architectures - Olgu, Kaan; Kenter, Tobias; Nunez-Yanez, Jose; Mcintosh-Smith, Simon (Proceedings of the 12th International Workshop on OpenCL and SYCL)

IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency - Ghafouri, Saeid; Razavi, Kamran; Salmani, Mehran; Sanaee, Alireza; Lorido Botran, Tania; Wang, Lin; Doyle, Joseph; Jamshidi, Pooyan (Journal of Systems Research (JSys))

Empowering Students for the Data-Driven World: A Qualitative Study of the Relevance of Learning about Data-Driven Technologies - Höper, Lukas; Schulte, Carsten (Informatics in Education)
In K-12 computing education, there is a need to identify and teach concepts that are relevant to understanding machine learning technologies. Studies of teaching approaches often evaluate whether students have learned the concepts. However, scant research has examined whether such concepts support understanding digital artefacts from everyday life and developing agency in a digital world. This paper presents a qualitative study that explores students’ perspectives on the relevance of learning concepts of data-driven technologies for navigating the digital world. The underlying approach of the study is data awareness, which aims to support students in understanding and reflecting on such technologies to develop agency in a data-driven world. This approach teaches students an explanatory model encompassing several concepts of the role of data in data-driven technologies. We developed an intervention and conducted retrospective interviews with students. Findings from the analysis of the interviews indicate that students can analyse and understand data-driven technologies from their everyday lives according to the central role of data. In addition, students’ answers revealed four areas of how learning about data-driven technologies becomes relevant to them. The paper concludes with a preliminary model suggesting how computing education can make concepts of data-driven technologies meaningful for students to understand and navigate the digital world.
Keywords: Computer Science Applications; Communication; Education; General Engineering

Noctua 2 Supercomputer - Bauer, Carsten; Kenter, Tobias; Lass, Michael; Mazur, Lukas; Meyer, Marius; Nitsche, Holger; Riebler, Heinrich; Schade, Robert; Schwarz, Michael; Winnwa, Nils; Wiens, Alex; Wu, Xin; et al. (Journal of large-scale research facilities)
Noctua 2 is a supercomputer operated at the Paderborn Center for Parallel Computing (PC2) at Paderborn University in Germany. Noctua 2 was inaugurated in 2022 and is an Atos BullSequana XH2000 system. It consists mainly of three node types: 1) CPU Compute nodes with AMD EPYC processors in different main memory configurations, 2) GPU nodes with NVIDIA A100 GPUs, and 3) FPGA nodes with Xilinx Alveo U280 and Intel Stratix 10 FPGA cards. While CPUs and GPUs are known off-the-shelf components in HPC systems, the operation of a large number of FPGA cards from different vendors and a dedicated FPGA-to-FPGA network are unique characteristics of Noctua 2. This paper describes in detail the overall setup of Noctua 2 and gives insights into the operation of the cluster from a hardware, software and facility perspective.
Keywords: Noctua 2; Supercomputer; FPGA; PC2; Paderborn Center for Parallel Computing

A Little Certainty is All We Need: Discovery and Synchronization Acceleration in Battery-Free IoT - Liu, Gaosheng; Nigade, Vinod; Bal, Henri; Wang, Lin (Proceedings of the 8th ACM Asia Pacific Workshop on Networking (APNET))

TARPS: A Toolbox for Enhancing Privacy and Security for Collaborative AR - Krings, Sarah Claudia; Yigitbas, Enes (Proceedings of the ACM on Human-Computer Interaction, EICS)

Augmented and Virtual Reality for Diet and Nutritional Education: A Systematic Literature Review - Yigitbas, Enes; Mazur, Janet (Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2024))

Development and Usability Evaluation of Transitional Cross-Reality Interfaces - Schmidt, Leonard; Yigitbas, Enes (Proceedings of the ACM on Human-Computer Interaction, EICS)

Gamification- and Virtual Reality-Based Learning Environment for UML Class Diagram Modeling - Yigitbas, Enes; Schmidt, Maximilian; Bucchiarone, Antonio; Bassanelli, Simone; Engels, Gregor (IEEE Global Engineering Education Conference 2024)

Effects of Human Avatar Representation in Virtual Reality on Inter-Brain Connections - Yigitbas, Enes; Kaltschmidt, Christian (Proceedings of the 8th International Conference on Artificial Intelligence and Virtual Reality (AIVR’24))

Interaction Techniques for Remote Maintenance in an AR Shared Environment - Krings, Sarah Claudia; Biermeier, Kai; Yigitbas, Enes (Proceedings of the 10th International Working Conference on Human-Centered Software Engineering (HCSE'24))

An Exploratory Study of Fear-Inducing Factors in Virtual Reality Experiences - Leichtweiß, Justus; Yigitbas, Enes (Proceedings of the 12th International Conference on Serious Games and Applications for Health (SeGAH'24))

Prototyping Cross-Reality Escape Rooms - Krois, Sebastian; Yigitbas, Enes (Proceedings of the 10th International Working Conference on Human-Centered Software Engineering (HCSE'24))

Developing a VR Factory Walkthrough for Use in Schools - Krings, Sarah Claudia; Yigitbas, Enes; Sauer, Stefan (Proceedings of the 10th International Working Conference on Human-Centered Software Engineering (HCSE'24))

Common pitfalls to avoid while using multiobjective optimization in machine learning - Akhter, Junaid; Fährmann, Paul David; Sonntag, Konstantin; Peitz, Sebastian (arXiv)

Evaluating Security Through Isolation and Defense in Depth - Bodden, Eric; Pottebaum, Jens; Fockel, Markus; Gräßler, Iris (IEEE Security & Privacy)
Keywords: Law; Electrical and Electronic Engineering; Computer Networks and Communications

Symbol-Specific Sparsification of Interprocedural Distributive Environment Problems - Karakaya, Kadiray; Bodden, Eric (Proceedings of the IEEE/ACM 46th International Conference on Software Engineering)
Previous work has shown that one can often greatly speed up static analysis by computing data flows not for every edge in the program’s control-flow graph but instead only along definition-use chains. This yields a so-called sparse static analysis. Recent work on SparseDroid has shown that specifically taint analysis can be “sparsified” with extraordinary effectiveness because the taint state of one variable does not depend on those of others. This allows one to soundly omit more flow-function computations than in the general case. In this work, we now assess whether this result carries over to the more generic setting of so-called Interprocedural Distributive Environment (IDE) problems. Opposed to taint analysis, IDE comprises distributive problems with large or even infinitely broad domains, such as typestate analysis or linear constant propagation. Specifically, this paper presents Sparse IDE, a framework that realizes sparsification for any static analysis that fits the IDE framework. We implement Sparse IDE in SparseHeros, as an extension to the popular Heros IDE solver, and evaluate its performance on real-world Java libraries by comparing it to the baseline IDE algorithm. To this end, we design, implement and evaluate a linear constant propagation analysis client on top of SparseHeros. Our experiments show that, although IDE analyses can only be sparsified with respect to symbols and not (numeric) values, Sparse IDE can nonetheless yield significantly lower runtimes and often also memory consumptions compared to the original IDE.

SootUp: A Redesign of the Soot Static Analysis Framework - Karakaya, Kadiray; Schott, Stefan; Klauke, Jonas; Bodden, Eric; Schmidt, Markus; Luo, Linghui; He, Dongjie (Tools and Algorithms for the Construction and Analysis of Systems)
Abstract Since its inception two decades ago, Soot has become one of the most widely used open-source static analysis frameworks. Over time it has been extended with the contributions of countless researchers. Yet, at the same time, the requirements for Soot have changed over the years and become increasingly at odds with some of the major design decisions that underlie it. In this work, we thus present SootUp , a complete reimplementation of Soot that seeks to fulfill these requirements with a novel design, while at the same time keeping elements that Soot users have grown accustomed to.

Detecting Security-Relevant Methods using Multi-label Machine Learning - Johnson, Oshando; Piskachev, Goran; Krishnamurthy, Ranjith; Bodden, Eric (Proceedings of the 46th International Conference on Software Engineering, IDE Workshop)
To detect security vulnerabilities, static analysis tools need to be configured with security-relevant methods. Current approaches can automatically identify such methods using binary relevance machine learning approaches. However, they ignore dependencies among security-relevant methods, over-generalize and perform poorly in practice. Additionally, users have to nevertheless manually configure static analysis tools using the detected methods. Based on feedback from users and our observations, the excessive manual steps can often be tedious, error-prone and counter-intuitive. In this paper, we present Dev-Assist, an IntelliJ IDEA plugin that detects security-relevant methods using a multi-label machine learning approach that considers dependencies among labels. The plugin can automatically generate configurations for static analysis tools, run the static analysis, and show the results in IntelliJ IDEA. Our experiments reveal that Dev-Assist's machine learning approach has a higher F1-Measure than related approaches. Moreover, the plugin reduces and simplifies the manual effort required when configuring and using static analysis tools.

TypeEvalPy: A Micro-benchmarking Framework for Python Type Inference Tools - Shivarpatna Venkatesh, Ashwin Prasad; Sabu, Samkutty; Wang, Jiawei; Mir, Amir M.; Li, Li; Bodden, Eric (Proceedings of the 46th International Conference on Software Engineering, Tool Demo Track)

Measuring Visual Attention Capacity Across xReality - Biermeier, Kai; Scharlau, Ingrid; Yigitbas, Enes (Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2024))

Exploring Custom FPGA Accelerators for DNN-based RF Fingerprinting - Henke, Luca-Sebastian

Towards New Realities: Implications of Personalized Online Layers in Our Daily Lives - Herder, Eelco; Stojko, Laura; Strecker, Jannis; Neumayr, Thomas; Yigitbas, Enes; Augstein, Mirjam (i-com - Journal of Interactive Media, Special Issue on “The Future of Human-Computer Interaction” (to appear))

Digitale Plattformen im industriellen Mittelstand

"I'm actually going to go and change these passwords": Analyzing the Usability of Credential Audit Interfaces in Password Managers - Kablo, Emiram; Kader, Katharina; Arias Cabarcos, Patricia (Extended Abstracts of the CHI Conference on Human Factors in Computing Systems)

Enabling Performance Portability for Shallow Water Equations on CPUs, GPUs, and FPGAs with SYCL - Büttner, Markus; Alt, Christoph; Kenter, Tobias; Köstler, Harald; Plessl, Christian; Aizinger, Vadym (Proceedings of the Platform for Advanced Scientific Computing Conference (PASC))

ESLM: Improving Entity Summarization by Leveraging Language Models - Firmansyah, Asep Fajar; Moussallem, Diego; Ngomo, Axel-Cyrille Ngonga (The Semantic Web)

Security Analysis of BigBlueButton and eduMEET - Heitmann, Nico; Siewert, Hendrik; Moog, Sven; Somorovsky, Juraj (Applied Cryptography and Network Security)
Video conferencing systems have become an indispensable part of our world. Using video conferencing systems implies the expectation that online meetings run as smoothly as in-person meetings. Thus, online meetings need to be just as secure and private as in-person meetings, which are secured against disruptive factors and unauthorized persons by physical access control mechanisms. To show the security dangers of conferencing systems and raise general awareness when using these technologies, we analyze the security of two widely used research and education open-source video conferencing systems: BigBlueButton and eduMEET. Because both systems are very different, we analyzed their architectures, considering the respective components with their main tasks, features, and user roles. In the following systematic security analyses, we found 50 vulnerabilities. These include broken access control, NoSQL injection, and denial of service (DoS). The vulnerabilities have root causes of different natures. While BigBlueButton has a lot of complexity due to many components, eduMEET, which is relatively young, focuses more on features than security. The sheer amount of results and the lack of prior work indicate a research gap that needs to be closed since video conferencing systems continue to play a significant role in research, education, and everyday life.

UpCy: Safely Updating Outdated Dependencies - Dann, Andreas Peter; Hermann, Ben; Bodden, Eric

On the Universal Transformation of Data-Driven Models to Control Systems - Peitz, Sebastian; Bieker, Katharina (Automatica)
As in almost every other branch of science, the major advances in data science and machine learning have also resulted in significant improvements regarding the modeling and simulation of nonlinear dynamical systems. It is nowadays possible to make accurate medium to long-term predictions of highly complex systems such as the weather, the dynamics within a nuclear fusion reactor, of disease models or the stock market in a very efficient manner. In many cases, predictive methods are advertised to ultimately be useful for control, as the control of high-dimensional nonlinear systems is an engineering grand challenge with huge potential in areas such as clean and efficient energy production, or the development of advanced medical devices. However, the question of how to use a predictive model for control is often left unanswered due to the associated challenges, namely a significantly higher system complexity, the requirement of much larger data sets and an increased and often problem-specific modeling effort. To solve these issues, we present a universal framework (which we call QuaSiModO: Quantization-Simulation-Modeling-Optimization) to transform arbitrary predictive models into control systems and use them for feedback control. The advantages of our approach are a linear increase in data requirements with respect to the control dimension, performance guarantees that rely exclusively on the accuracy of the predictive model, and only little prior knowledge requirements in control theory to solve complex control problems. In particular the latter point is of key importance to enable a large number of researchers and practitioners to exploit the ever increasing capabilities of predictive models for control in a straight-forward and systematic fashion.

Examples of AI-based Assistance Systems in context of Model-Based Systems Engineering - Schrader, Elena; Bernijazov, Ruslan; Foullois, Marc; Hillebrand, Michael; Kaiser, Lydia; Dumitrescu, Roman (2022 IEEE International Symposium on Systems Engineering (ISSE))

Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs? - Richter, Cedric; Haltermann, Jan Frederik; Jakobs, Marie-Christine; Pauck, Felix; Schott, Stefan; Wehrheim, Heike (37th IEEE/ACM International Conference on Automated Software Engineering)

Benchmark Fuzzing for Android Taint Analyses - Schott, Stefan; Pauck, Felix (2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM))

Scaling Arbitrary Android App Analyses - Pauck, Felix (37th IEEE/ACM International Conference on Automated Software Engineering)

Updatable Privacy-Preserving Reputation System based on Blockchain - Pilot, Matthias

Epistemic Programming - Hüsing, Sven; Schulte, Carsten; Winkelnkemper, Felix (Computer Science Education)

Gathering a Euclidean Closed Chain of Robots in Linear Time and Improved Algorithms for Chain-Formation - Castenow, Jannik; Harbig, Jonas; Jung, Daniel; Knollmann, Till; Meyer auf der Heide, Friedhelm (Theoretical Computer Science)
Keywords: General Computer Science; Theoretical Computer Science

Model Generation For Java Frameworks - Luo, Linghui; Piskachev, Goran; Krishnamurthy, Ranjith; Dolby, Julian; Schäf, Martin; Bodden, Eric (IEEE International Conference on Software Testing, Verification and Validation (ICST))

Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis - Shivarpatna Venkatesh, Ashwin Prasad; Wang, Jiawei; Li, Li; Bodden, Eric (IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER))

End-User Development of Interactive Web-Based Virtual Reality Scenes - Yigitbas, Enes; Klauke, Jonas; Gottschalk, Sebastian; Engels, Gregor (Journal on Computer Languages (COLA))

Enhancing Robot Programming through Digital Twin and Augmented Reality - Yigitbas, Enes; Engels, Gregor (56th Hawaii International Conference on System Science (HICSS 2023))

Towards Enhanced Guiding Mechanisms in VR Training through Process Mining - Yigitbas, Enes; Krois, Sebastian; Gottschalk, Sebastian; Engels, Gregor (Proceedings of the 7th International Conference on Human Computer Interaction Theory and Applications (HUCAPP'23))

A Unifying Approach to Efficient (Near-)Gathering of Disoriented Robots with Limited Visibility - Castenow, Jannik; Harbig, Jonas; Jung, Daniel; Kling, Peter; Knollmann, Till; Meyer auf der Heide, Friedhelm (Proceedings of the 26th International Conference on Principles of Distributed Systems (OPODIS))

Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs - Werner, Stefan; Peitz, Sebastian (arXiv:2302.07160)
The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional LSTM with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstarte our findings.

Quantum space, ground space traversal, and how to embed multi-prover interactive proofs into unentanglement - Gharibian, Sevag; Rudolph, Dorian (14th Innovations in Theoretical Computer Science (ITCS))
Savitch's theorem states that NPSPACE computations can be simulated in PSPACE. We initiate the study of a quantum analogue of NPSPACE, denoted Streaming-QCMASPACE (SQCMASPACE), where an exponentially long classical proof is streamed to a poly-space quantum verifier. Besides two main results, we also show that a quantum analogue of Savitch's theorem is unlikely to hold, as SQCMASPACE=NEXP. For completeness, we introduce Streaming-QMASPACE (SQMASPACE) with an exponentially long streamed quantum proof, and show SQMASPACE=QMA_EXP (quantum analogue of NEXP). Our first main result shows, in contrast to the classical setting, the solution space of a quantum constraint satisfaction problem (i.e. a local Hamiltonian) is always connected when exponentially long proofs are permitted. For this, we show how to simulate any Lipschitz continuous path on the unit hypersphere via a sequence of local unitary gates, at the expense of blowing up the circuit size. This shows quantum error-correcting codes can be unable to detect one codeword erroneously evolving to another if the evolution happens sufficiently slowly, and answers an open question of [Gharibian, Sikora, ICALP 2015] regarding the Ground State Connectivity problem. Our second main result is that any SQCMASPACE computation can be embedded into "unentanglement", i.e. into a quantum constraint satisfaction problem with unentangled provers. Formally, we show how to embed SQCMASPACE into the Sparse Separable Hamiltonian problem of [Chailloux, Sattath, CCC 2012] (QMA(2)-complete for 1/poly promise gap), at the expense of scaling the promise gap with the streamed proof size. As a corollary, we obtain the first systematic construction for obtaining QMA(2)-type upper bounds on arbitrary multi-prover interactive proof systems, where the QMA(2) promise gap scales exponentially with the number of bits of communication in the interactive proof.

On the structure of regularization paths for piecewise differentiable regularization terms - Gebken, Bennet; Bieker, Katharina; Peitz, Sebastian (Journal of Global Optimization)
Regularization is used in many different areas of optimization when solutions are sought which not only minimize a given function, but also possess a certain degree of regularity. Popular applications are image denoising, sparse regression and machine learning. Since the choice of the regularization parameter is crucial but often difficult, path-following methods are used to approximate the entire regularization path, i.e., the set of all possible solutions for all regularization parameters. Due to their nature, the development of these methods requires structural results about the regularization path. The goal of this article is to derive these results for the case of a smooth objective function which is penalized by a piecewise differentiable regularization term. We do this by treating regularization as a multiobjective optimization problem. Our results suggest that even in this general case, the regularization path is piecewise smooth. Moreover, our theory allows for a classification of the nonsmooth features that occur in between smooth parts. This is demonstrated in two applications, namely support-vector machines and exact penalty methods.

Beep-and-Sleep: Message and Energy Efficient Set Cover - Götte, Thorsten; Kolb, Christina; Scheideler, Christian; Werthmann, Julian (Theor. Comput. Sci.)

Supporting Construction and Architectural Visualization through BIM and AR/VR: A Systematic Literature Review - Yigitbas, Enes; Nowosad, Alexander; Engels, Gregor (Proceedings of the 19th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2023))

Towards reliable data-based optimal and predictive control using extended DMD - Schaller, Manuel; Worthmann, Karl; Philipp, Friedrich; Peitz, Sebastian; Nüske, Feliks (IFAC-PapersOnLine)
We present an approach for guaranteed constraint satisfaction by means of data-based optimal control, where the model is unknown and has to be obtained from measurement data. To this end, we utilize the Koopman framework and an eDMD-based bilinear surrogate modeling approach for control systems to show an error bound on predicted observables, i.e., functions of the state. This result is then applied to the constraints of the optimal control problem to show that satisfaction of tightened constraints in the purely data-based surrogate model implies constraint satisfaction for the original system.

Online load balancing on uniform machines with limited migration - Maack, Marten (Operations Research Letters)
Keywords: Applied Mathematics; Industrial and Manufacturing Engineering; Management Science and Operations Research; Software

A Formal Comparison of Advanced Digital Signature Primitives - Schürmann, Patrick

The Complexity of Translationally Invariant Problems beyond Ground State Energies - Gharibian, Sevag; Watson, James; Bausch, Johannes (Proceedings of the 40th International Symposium on Theoretical Aspects of Computer Science (STACS))

MAAS: Hiding Trojans in Approximate Circuits - Ahmed, Qazi Arbab; Awais, Muhammad; Platzner, Marco (The 24th International Symposium on Quality Electronic Design (ISQED'23), San Francisco, Califorina USA)

Minimum Edge Cuts in Overlay Networks - Schweichhart, Jonas

Unifying Gathering Protocols for Swarms of Mobile Robots - Castenow, Jannik; Harbig, Jonas; Meyer auf der Heide, Friedhelm (Lecture Notes in Computer Science)

Model-driven Collaborative Design of Professional Education Programmes With Extended Online Whiteboards - Wolters, Dennis; Engels, Gregor (MODELSWARD'23)

Cooperative Android App Analysis - Pauck, Felix

Routing Schemes for Hybrid Communication Networks - Werthmann, Julian; Scheideler, Christian; Coy, Sam; Czumaj, Artur; Schneider, Philipp

Designing Business Reputation Ecosystems — A Method for Issuing and Trading Monetary Ratings on a Blockchain - Hemmrich, Simon; Bobolz, Jan; Beverungen, Daniel; Blömer, Johannes (ECIS 2023 Research Papers)
Market transactions are subject to information asymmetry about the delivered value proposition, causing transaction costs and adverse market effects among buyers and sellers. Information systems research has investigated how review systems can reduce information asymmetry in business-to-consumer markets. However, these systems cannot be readily applied to business-to-business markets, are vulnerable to manipulation, and suffer from conceptual weak spots since they use textual data or star ratings. Building on design science research, we conceptualize a new class of reputation systems based on monetary-based payments as quantitative ratings for each transaction stored on a blockchain. Using cryptography, we show that our system assures content confidentiality so that buyers can share and sell their ratings selectively, establishing a reputation ecosystem. Our prescriptive insights advance the design of reputation systems and offer new paths to understanding the antecedents, dynamics, and consequences to reduce information asymmetry in B2B transactions.

Development and Evaluation of a Model-Based UI Prototyping Experimentation Approach - N., N.

Two Sparsification Strategies for Accelerating Demand-Driven Pointer Analysis - Karakaya, Kadiray; Bodden, Eric (2023 IEEE Conference on Software Testing, Verification and Validation (ICST))

Privacy-Preserving Collection and Evaluation of Log Files - Koch, Angelina

IngridKG: A FAIR Knowledge Graph of Graffiti - Sherif, Mohamed Ahmed; da Silva, Ana Alexandra Morim; Pestryakova, Svetlana; Ahmed, Abdullah Fathi; Niemann, Sven; Ngomo, Axel-Cyrille Ngonga (Scientific Data)
Abstract Graffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project ( Ingrid ) closes this gap by dealing with graffiti image collections that have been made available to the project for public use. Within Ingrid , the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid access to a comprehensive data source on Ingrid targeted especially by researchers. In particular, we present Ingrid KG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update Ingrid KG by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion, link discovery and data fusion approaches to the original data. The current version of Ingrid KG contains 460,640,154 triples and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications.
Keywords: Library and Information Sciences; Statistics; Probability and Uncertainty; Computer Science Applications; Education; Information Systems; Statistics and Probability

Local Protocols for Contracting and Expanding Robot Formation Problems - Castenow, Jannik

Online Algorithms for Allocating Heterogeneous Resources - Knollmann, Till

Approximation Algorithms for Fair Range Clustering - Hotegni, Sedjro Salomon; Mahabadi, Sepideh; Vakilian, Ali (Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023.)
Keywords: Fair range clustering

We Really Need to Talk About Session Tickets: A Large-Scale Analysis of Cryptographic Dangers with TLS Session Tickets - Hebrok, Sven Niclas; Nachtigall, Simon; Maehren, Marcel; Erinola, Nurullah; Merget, Robert; Somorovsky, Juraj; Schwenk, Jörg (32nd USENIX Security Symposium)

Data-Driven Product Management: A Practitioner-Driven Research Agenda - Grigoryan, Khoren; Fichtler, Timm; Schreiner, Nick; Rabe, Martin; Panzner, Melina; Kühn, Arno; Dumitrescu, Roman; Koldewey, Christian (Procedia CIRP 33)
The global megatrends of digitization and sustainability lead to new challenges for the design and management of technical products in industrial companies. Product management - as the bridge between market and company - has the task to absorb and combine the manifold requirements and make the right product-related decisions. In the process, product management is confronted with heterogeneous information, rapidly changing portfolio components, as well as increasing product, and organizational complexity. Combining and utilizing data from different sources, e.g., product usage data and social media data leads to promising potentials to improve the quality of product-related decisions. In this paper, we reinforce the need for data-driven product management as an interdisciplinary field of action. The state of data-driven product management in practice was analyzed by conducting workshops with six manufacturing companies and hosting a focus group meeting with experts from different industries. We investigate the expectations and derive requirements leading us to open research questions, a vision for data-driven product management, and a research agenda to shape future research efforts.
Keywords: Product Management; Data Analytics; Data-Driven Design; Product-related data; Lifecycle Data; Tool-support

Interview Study on Strategy Options for Platform Operation in B2B Markets - Özcan, Leon; Fichtler, Timm; Kasten, Benjamin; Koldewey, Christian; Dumitrescu, Roman
Keywords: Digital Platform; Platform Strategy; Strategic Management; Platform Life Cycle; Interview Study; Business Model; Business-to-Business; Two-sided Market; Multi-sided Market

Neural Class Expression Synthesis - KOUAGOU, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023))
Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis
Keywords: Neural network; Concept learning; Description logics

Counterfactual Explanations for Concepts in ELH - Sieger, Leonie Nora; Heindorf, Stefan; Blübaum, Lukas; Ngonga Ngomo, Axel-Cyrille (arXiv:2301.05109)
Knowledge bases are widely used for information management on the web, enabling high-impact applications such as web search, question answering, and natural language processing. They also serve as the backbone for automatic decision systems, e.g. for medical diagnostics and credit scoring. As stakeholders affected by these decisions would like to understand their situation and verify fair decisions, a number of explanation approaches have been proposed using concepts in description logics. However, the learned concepts can become long and difficult to fathom for non-experts, even when verbalized. Moreover, long concepts do not immediately provide a clear path of action to change one's situation. Counterfactuals answering the question "How must feature values be changed to obtain a different classification?" have been proposed as short, human-friendly explanations for tabular data. In this paper, we transfer the notion of counterfactuals to description logics and propose the first algorithm for generating counterfactual explanations in the description logic $\mathcal{ELH}$. Counterfactual candidates are generated from concepts and the candidates with fewest feature changes are selected as counterfactuals. In case of multiple counterfactuals, we rank them according to the likeliness of their feature combinations. For evaluation, we conduct a user survey to investigate which of the generated counterfactual candidates are preferred for explanation by participants. In a second study, we explore possible use cases for counterfactual explanations.

Identifizierung quantifizierbarer Bewertungsinhalte und -kategorien mittels Text Mining - Kersting, Joschka
Reading between the lines has so far been reserved for humans. The present dissertation addresses this research gap using machine learning methods. Implicit expressions are not comprehensible by computers and cannot be localized in the text. However, many texts arise on interpersonal topics that, unlike commercial evaluation texts, often imply information only by means of longer phrases. Examples are the kindness and the attentiveness of a doctor, which are only paraphrased (“he didn’t even look me in the eye”). The analysis of such data, especially the identification and localization of implicit statements, is a research gap (1). This work uses so-called Aspect-based Sentiment Analysis as a method for this purpose. It remains open how the aspect categories to be extracted can be discovered and thematically delineated based on the data (2). Furthermore, it is not yet explored how a collection of tools should look like, with which implicit phrases can be identified and thus made explicit (3). Last, it is an open question how to correlate the identified phrases from the text data with other data, including the investigation of the relationship between quantitative scores (e.g., school grades) and the thematically related text (4). Based on these research gaps, the research question is posed as follows: Using text mining methods, how can implicit rating content be properly interpreted and thus made explicit before it is automatically categorized and quantified? The uniqueness of this dissertation is based on the automated recognition of implicit linguistic statements alongside explicit statements. These are identified in unstructured text data so that features expressed only in the text can later be compared across data sources, even though they were not included in rating categories such as stars or school grades. German-language physician ratings from websites in three countries serve as the sample domain. The solution approach consists of data creation, a pipeline for text processing and analyses based on this. In the data creation, aspect classes are identified and delineated across platforms and marked in text data. This results in six datasets with over 70,000 annotated sentences and detailed guidelines. The models that were created based on the training data extract and categorize the aspects. In addition, the sentiment polarity and the evaluation weight, i. e., the importance of each phrase, are determined. The models, which are combined in a pipeline, are used in a prototype in the form of a web application. The analyses built on the pipeline quantify the rating contents by linking the obtained information with further data, thus allowing new insights. As a result, a toolbox is provided to identify quantifiable rating content and categories using text mining for a sample domain. This is used to evaluate the approach, which in principle can also be adapted to any other domain.